Section: Overall Objectives
Scientific Context
Critical problems of the 21st century like the search for highly energy efficient or even carbonneutral, and costefficient systems, or the design of new molecules against extensively drugresistant bacteria crucially rely on the resolution of challenging numerical optimization problems. Such problems typically depend on noisy experimental data or involve complex numerical simulations such that derivatives are not useful or not available and the function is seen as a blackbox.
Many of those optimization problems are in essence multiobjective—one needs to optimize simultaneously several conflicting objectives like minimizing the cost of an energy network and maximizing its reliability—and most of the challenging blackbox problems are nonconvex, nonsmooth and combine difficulties related to illconditioning, nonseparability, and ruggedness (a term that characterizes functions that can be nonsmooth but also noisy or multimodal). Additionally the objective function can be expensive to evaluate, that is one function evaluation can take several minutes to hours (it can involve for instance a CFD simulation).
In this context, the use of randomness combined with proper adaptive mechanisms that particularly satisfy several invariance properties (affine invariance, invariance to monotonic transformations) has proven to be one key component for the design of robust global numerical optimization algorithms [35], [25].
The field of adaptive stochastic optimization algorithms has witnessed some important progress over the past 15 years. On the one hand, subdomains like mediumscale unconstrained optimization may be considered as “solved” (particularly, the CMAES algorithm, an instance of Evolution Strategy (ES) algorithms, stands out as stateoftheart method) and considerably better standards have been established in the way benchmarking and experimentation are performed. On the other hand, multiobjective populationbased stochastic algorithms became the method of choice to address multiobjective problems when a set of some best possible compromises is thought for. In all cases, the resulting algorithms have been naturally transferred to industry (the CMAES algorithm is now regularly used in companies such as Bosch, Total, ALSTOM, ...) or to other academic domains where difficult problems need to be solved such as physics, biology [38], geoscience [31], or robotics [33]).
Very recently, ES algorithms attracted quite some attention in Machine Learning with the OpenAI article Evolution Strategies as a Scalable Alternative to Reinforcement Learning. It is shown that the training time for difficult reinforcement learning benchmarks could be reduced from 1 day (with standard RL approaches) to 1 hour using ES [36]. (The key behind such an improvement is the parallelization of the algorithm (on thousands of CPUs) that is done in such a way that the communication between the different workers is reduced to only exchanging a vector of permutation of small length (typically less than 100) containing the ranking of candidate solutions on the function to be optimized. In contrast, parallelization of backpropagation requires to exchange the gradient vector of the size of the problem (of the order of ${10}^{6}$). This reduced communication time is an important factor for the important speedup.) A few years ago, another impressive application of CMAES, how “Computer Sim Teaches Itself To Walk Upright” (published at the conference SIGGRAPH Asia 2013) was presented in the press in the UK.
Several of those important advances around adaptive stochastic optimization algorithms are relying to a great extent on works initiated or achieved by the founding members of RandOpt particularly related to the CMAES algorithm and to the Comparing Continuous Optimizer (COCO) platform.
Yet, the field of adaptive stochastic algorithms for blackbox optimization is relatively young compared to the “classical optimization” field that includes convex and gradientbased optimization. For instance, the stateofthe art algorithms for unconstrained gradient based optimization like quasiNewton methods (e.g. the BFGS method) date from the 1970s [24] while the stochastic derivativefree counterpart, CMAES dates from the early 2000s [26]. Consequently, in some subdomains with important practical demands, not even the most fundamental and basic questions are answered:

This is the case of constrained optimization where one needs to find a solution ${x}^{*}\in {\mathbb{R}}^{n}$ minimizing a numerical function ${min}_{x\in {\mathbb{R}}^{n}}f\left(x\right)$ while respecting a number of constraints $m$ typically formulated as ${g}_{i}\left({x}^{*}\right)\le 0$ for $i=1,...,m$. Only recently, the fundamental requirement of linear convergence (In optimization, linear convergence for an algorithm whose estimate of the optimum ${x}^{*}$ of $f$ at iteration $t$ is denoted ${x}_{t}$, refers to a convergence where after a certain time (usually once the initialization is forgotten) the following typically holds: $\parallel {x}_{t+1}{x}^{*}\parallel \le c\parallel {x}_{t}{x}^{*}\parallel $ where $c<1$. This type of convergence is also called geometric. In the case of stochastic algorithms, there exist different definitions of linear convergence (depending on whether we consider the expectation of the sequence or we want a statement that holds with high probability) not strictly equivalent but that always translate the idea that the distance to the optimum at iteration $t+1$ is a fraction of the distance to the optimum at iteration $t$.), as in the unconstrained case, has been clearly stated [16].

In multiobjective optimization, most of the research so far has been focusing on how to select candidate solutions from one iteration to the next one. The difficult question of how to generate effectively new solutions is not yet answered in a proper way and we know today that simply applying operators from singleobjective optimization may not be effective with the current best selection strategies. As a comparison, in the singleobjective case, the question of selection of candidate solutions was already solved in the 1980s and 15 more years were needed to solve the trickier question of an effective adaptive strategy to generate new solutions.

With the current demand to solve larger and larger optimization problems (e.g. in the domain of deep learning), optimization algorithms that scale linearly (in terms of internal complexity, memory and number of function evaluations to reach an $\u03f5$ball around the optimum) with the problem dimension are nowadays needed. Only recently, first proposals of how to reduce the quadratic scaling of CMAES have been made without a clear view of what can be achieved in the best case in practice. These later variants apply to optimization problems with thousands of variables. The question of designing randomized algorithms capable to handle efficiently problems with one or two orders of magnitude more variables is still largely open.

For expensive optimization, standard methods are so called Bayesian optimization (BO) algorithms often based on Gaussian processes. Commonly used examples of BO algorithms are EGO [30], SMAC [28], Spearmint [37], or TPE [19] which are implemented in different libraries. Yet, our experience with a popular method like EGO is that many important aspects to come up with a good implementation rely on insider knowledge and are not standard across implementations. Two EGO implementations can differ for example in how they perform the initial design, which bandwidth for the Gaussian kernel is used, or which strategy is taken to optimize the expected improvement.
Additionally, the development of stochastic adaptive methods for blackbox optimization has been mainly driven by heuristics and practice—rather than a general theoretical framework—validated by intensive computational simulations. Undoubtedly, this has been an asset as the scope of possibilities for design was not restricted by mathematical frameworks for proving convergence. In effect, powerful stochastic adaptive algorithms for unconstrained optimization like the CMAES algorithm emerged from this approach. At the same time, naturally, theory strongly lags behind practice. For instance, the striking performances of CMAES empirically observed contrast with how little is theoretically proven on the method. This situation is clearly not satisfactory. On the one hand, theory generally lifts performance assessment from an empirical level to a conceptual one, rendering results independent from the problem instances where they have been tested. On the other hand, theory typically provides insights that change perspectives on some algorithm components. Also theoretical guarantees generally increase the trust in the reliability of a method and facilitate the task to make it accepted by wider communities.
Finally, as discussed above, the development of novel blackbox algorithms strongly relies on scientific experimentation, and it is quite difficult to conduct proper and meaningful experimental analysis. This is well known for more than two decades now and summarized in this quote from Johnson in 1996
“the field of experimental analysis is fraught with pitfalls. In many ways, the implementation of an algorithm is the easy part. The hard part is successfully using that implementation to produce meaningful and valuable (and publishable!) research results.” [29]
Since then, quite some progress has been made to set better standards in conducting scientific experiments and benchmarking. Yet, some domains still suffer from poor benchmarking standards and from the generic problem of the lack of reproducibility of results. For instance, in multiobjective optimization, it is (still) not rare to see comparisons between algorithms made by solely visually inspecting Pareto fronts after a fixed budget. In Bayesian optimization, good performance seems often to be due to insider knowledge not always well described in papers.